Bayesian nonparametric clusterings in relational and high-dimensional settings with applications in bioinformatics.

Recent advances in high throughput methodologies offer researchers the ability to understand complex systems via high dimensional and multi-relational data. One example is the realm of molecular biology where disparate data (such as gene sequence, gene expression, and interaction information) are available for various snapshots of biological systems. This type of high dimensional and multirelational data allows for unprecedented detailed analysis, but also presents challenges in accounting for all the variability. High dimensional data often has a multitude of underlying relationships, each represented by a separate clustering structure, where the number of structures is typically unknown a priori. To address the challenges faced by traditional clustering methods on high dimensional and multirelational data, we developed three feature selection and cross-clustering methods: 1) infinite relational model with feature selection (FIRM) which incorporates the rich information of multirelational data; 2) Bayesian Hierarchical Cross-Clustering (BHCC), a deterministic approximation to Cross Dirichlet Process mixture (CDPM) and to cross-clustering; and 3) randomized approximation (RBHCC), based on a truncated hierarchy. An extension of BHCC, Bayesian Congruence Measuring (BCM), is proposed to measure incongruence between genes and to identify sets of congruent loci with identical evolutionary histories. We adapt our BHCC algorithm to the inference of BCM, where the intended structure of each view (congruent loci) represents consistent evolutionary processes. We consider an application of FIRM on categorizing mRNA and microRNA. The model uses latent structures to encode the expression pattern and the gene ontology annotations. We also apply FIRM to recover the categories of ligands and proteins, and to predict unknown drug-target interactions, where latent categorization structure encodes drug-target interaction, chemical compound similarity, and amino acid sequence similarity. BHCC and RBHCC are shown to have improved predictive performance (both in terms of cluster membership and missing value prediction) compared to traditional clustering methods. Our results suggest that these novel approaches to integrating multi-relational information have a promising future in the biological sciences where incorporating data related to varying features is often regarded as a daunting task

Bibliometric and visual analysis of RAN methylation in cardiovascular disease

BackgroundRNA methylation is associated with cardiovascular disease (CVD) occurrence and development. The purpose of this study is to visually analyze the results and research trends of global RNA methylation in CVD.MethodsArticles and reviews on RNA methylation in CVD published before 6 November 2022 were searched in the Web of Science Core Collection. Visual and statistical analysis was performed using CiteSpace 1.6.R4 advanced and VOSviewer 1.6.18.ResultsThere were 847 papers from 1,188 institutions and 63 countries/regions. Over approximately 30 years, there was a gradual increase in publications and citations on RNA methylation in CVD. America and China had the highest output (284 and 259 papers, respectively). Nine of the top 20 institutions that published articles were from China, among which Fudan University represented the most. The International Journal of Molecular Sciences was the journal with the most studies. Nature was the most co-cited journal. The most influential writers were Zhang and Wang from China and Mathiyalagan from the United States. After 2015, the primary keywords were cardiac development, heart, promoter methylation, RNA methylation, and N6-methyladenosine. Nuclear RNA, m6A methylation, inhibition, and myocardial infarction were the most common burst keywords from 2020 to the present.ConclusionsA bibliometric analysis reveals research hotspots and trends of RNA methylation in CVD. The regulatory mechanisms of RNA methylation related to CVD and the clinical application of their results, especially m6A methylation, are likely to be the focus of future research

Complex Networks Approach for Analyzing the Correlation of Traditional Chinese Medicine Syndrome Evolvement and Cardiovascular Events in Patients with Stable Coronary Heart Disease

This is a multicenter prospective cohort study to analyze the correlation of traditional Chinese medicine (TCM) syndrome evolvement and cardiovascular events in patients with stable coronary heart disease (CHD). The impact of syndrome evolvement on cardiovascular events during the 6-month and 12-month follow-up was analyzed using complex networks approach. Results of verification using Chi-square test showed that the occurrence of cardiovascular events was positively correlated with syndrome evolvement when it evolved from toxic syndrome to Qi deficiency, blood stasis, or sustained toxic syndrome, when it evolved from Qi deficiency to blood stasis, toxic syndrome, or sustained Qi deficiency, and when it evolved from blood stasis to Qi deficiency. Blood stasis, Qi deficiency, and toxic syndrome are important syndrome factors for stable CHD. There are positive correlations between cardiovascular events and syndrome evolution from toxic syndrome to Qi deficiency or blood stasis, from Qi deficiency to blood stasis, or toxic syndrome and from blood stasis to Qi deficiency. These results indicate that stable CHD patients with pathogenesis of toxin consuming Qi, toxin leading to blood stasis, and mutual transformation of Qi deficiency and blood stasis are prone to recurrent cardiovascular events

Bayesian Hierarchical Cross-Clustering

Most clustering algorithms assume that all dimensions of the data can be described by a single structure. Cross-clustering (or multiview clustering) allows multiple structures, each applying to a subset of the dimensions. We present a novel approach to crossclustering, based on approximating the solution to a Cross Dirichlet Process mixture (CDPM) model [Shafto et al., 2006, Mansinghka et al., 2009]. Our bottom-up, deterministic approach results in a hierarchical clustering of dimensions, and at each node, a hierarchical clustering of data points. We also present a randomized approximation, based on a truncated hierarchy, that scales linearly in the number of levels. Results on synthetic and real-world data sets demonstrate that the cross-clustering based algorithms perform as well or better than the clustering based algorithms, our deterministic approaches models perform as well as the MCMC-based CDPM, and the randomized approximation provides a remarkable speedup relative to the full deterministic approximation with minimal cost in predictive error.

Panax quinquefolius saponins combined with dual antiplatelet therapy enhanced platelet inhibition with alleviated gastric injury via regulating eicosanoids metabolism

Abstract Background Panax quinquefolius saponin (PQS) was shown beneficial against platelet adhesion and for gastroprotection. This study aimed to investigate the integrated efficacy of PQS with dual antiplatelet therapy (DAPT) on platelet aggregation, myocardial infarction (MI) expansion and gastric injury in a rat model of acute MI (AMI) and to explore the mechanism regarding arachidonic acid (AA)-derived eicosanoids metabolism. Methods Wistar rats were subjected to left coronary artery occlusion to induce AMI model followed by treatment with DAPT, PQS or the combined therapy. Platelet aggregation was measured by light transmission aggregometry. Infarct size, myocardial histopathology was evaluated by TTC and H&E staining, respectively. Gastric mucosal injury was examined by scanning electron microscope (SEM). A comprehensive eicosanoids profile in plasma and gastric mucosa was characterized by liquid chromatography-mass spectrometer-based lipidomic analysis. Results PQS+DAPT further decreased platelet aggregation, lessened infarction and attenuated cardiac injury compared with DAPT. Plasma lipidomic analysis revealed significantly increased synthesis of epoxyeicosatrienoic acid (EET) and prostaglandin (PG) I2 (potent inhibitors for platelet adhesion and aggregation) while markedly decreased thromboxane (TX) A2 (an agonist for platelet activation and thrombosis) by PQS+DAPT, relative to DAPT. DAPT induced overt gastric mucosal damage, which was attenuated by PQS co-administration. Mucosal gastroprotective PGs (PGE2, PGD2 and PGI2) were consistently increased after supplementation of PQS+DAPT. Conclusions Collectively, PQS+DAPT showed synergistic effect in platelet inhibition with ameliorated MI expansion partially through upregulation of AA/EET and AA/PGI2 synthesis while suppression of AA/TXA2 metabolism. PQS attenuated DAPT-induced gastric injury, which was mechanistically linked to increased mucosal PG production

Total ginsenosides decrease Aβ production through activating PPARγ

Introduction: Total ginsenosides (TG), the major active constituents of ginseng, have been proven to be beneficial in treatment of Alzheimer’s disease (AD). However, the underlying mechanism of TG remains unclear. Methods: APP/PS1 mice and N2a/APP695 cells were used as in vivo and in vitro model, respectively. Morris water maze (MWM) was used to investigate behavioral changes of mice; neuronal pathological changes were assessed by hematoxylin and eosin (H&E) and nissl staining; immunofluorescence staining was used to examine amyloid beta (Aβ) deposition; Western blotting and quantitative real-time polymerase chain reaction (qRT-PCR) were used to examine the expression of relative amyloidogenic genes and proteins. Moreover, the antagonist of PPARγ, GW9662, was used to determine whether the effects of TG on Aβ production were associated with PPARγ activity. Results: TG treatment increased the spatial learning and memory abilities of APP/PS1 mice while decreasing the Aβ accumulation in the cortex and hippocampus. In N2a/APP695 cells, TG treatment attenuated the secretion of Aβ1–40 and Aβ1–42 acting as an PPARγ agonist by inhibiting the translocation of NF-κB p65. Additionally, TG treatment also decreased the expression of amyloidogenic pathway related gene BACE1, PS1 and PS2. Conclusions: TG treatment reduced the production of Aβ both in vivo and in vitro. Activating PPARγ might be a potential therapeutic target of TG in facilitating Aβ clearance and ameliorating cognitive deficiency in APP/PS1 mice

Mechanisms with network pharmacology approach of Ginsenosides in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory loss, cognitive disorder, language dysfunction, and mental disability. The main neuropathological changes in AD mainly include amyloid plaque deposition, neurofibrillary tangles, synapse loss, and neuron reduction. However, the current anti-AD drugs do not demonstrate a favorable effect in altering the pathological course of AD. Moreover, long-term use of these drugs is usually accompanied with various side effects. Ginsenosides are the major active constituents of ginseng and have protective effects on AD through various mechanisms in both in vivo and in vitro studies. In this review, we focused on discussing the therapeutic potential effects and the mechanisms of pharmacological activities of ginsenosides in AD, to provide new insight for further research and clinical application of ginsenosides in the future. Recent studies on the pharmacological effects and mechanisms of ginsenosides were retrieved from Chinese National Knowledge Infrastructure, National Science and Technology Library, Wanfang Data, Elsevier, ScienceDirect, PubMed, SpringerLink, and the Web of Science database up to April 2023 using relevant keywords. Network pharmacology and bioinformatics analysis were used to predict the therapeutic effects and mechanisms of ginsenosides against AD. Ginsenosides presented a wide range of therapeutic and biological activities, including alleviating Aβ deposition, decreasing tau hyperphosphorylation, regulating the cholinergic system, resisting oxidative stress, modulating Ca2+ homeostasis, as well as anti-inflammation and anti-apoptosis in neurons, respectively. For further developing the therapeutic potential as well as clinical applications, the network pharmacology approach was combined with a summary of published studies

ITIH4: A New Potential Biomarker of “Toxin Syndrome” in Coronary Heart Disease Patient Identified with Proteomic Method

Objective. This trial aims to look for the protein biomarker of “toxin syndrome” of CHD patients. Methods. We have performed two trials in this paper. The first trial was a randomized controlled trial (RCT) of the plasma proteome in unstable angina (UA) patients by Maldi-Tof Mass. The second trial was a nested case-control study in 1503 stable CHD patients with one-year followup for acute cardiovascular events (ACEs). Results. In the RCT study, 12 protein spots were found to be the differential protein for the significant differences between the difference of before and after treatment in group A and group B; 2 of them (3207.37 Da and 4279.95 Da) was considered to be unique to “toxin syndrome” for being differential proteins of group B but not group A. These 2 spots were identified as Isoform 1 of Fibrinogen alpha chain precursor (FGA, 3207.37 Da) and Isoform 2 of inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4, 4279.95 Da), respectively. In the nested case-control study, the result of Western blot demonstrated that protein expression of ITIH4 in the group with followup ACEs was significantly lower than the matched group without followup ACEs (P=0.027). Conclusion. ITIH4 might be a new potential biomarker of CHD “toxin syndrome” in TCM, indicating the potential role in early identifying high-risk CHD patients in stable period